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Related Experiment Video

Updated: Jun 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system.

Sheharyar Khan1, Zheng Jiangbin1, Farhan Ullah1

  • 1School of Software, Northwestern Polytechnical University, Xi'an, China.

Peerj. Computer Science
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

The Trusted IoT Big Data Analytics (TIBDA) framework enhances smart home security and performance using a hybrid cryptosystem and AI. It significantly improves response time, security, and trustworthiness for Internet of Things (IoT) devices.

Keywords:
Artificial intelligenceBig dataBlockchainCryptographyData security and privacyHybrid computingInternet of Things (IoT)Machine learningOffloadingSmart home

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Cloud computing facilitates resource access but faces scalability and security challenges with the Internet of Things (IoT).
  • Smart home technologies require robust data security and privacy solutions for dynamic offloading across edge, fog, and cloud computing environments.
  • Existing IoT systems struggle with data security, privacy, processing speed, storage limitations, and analytics within networked devices.

Purpose of the Study:

  • To introduce the Trusted IoT Big Data Analytics (TIBDA) framework to address smart home data security and privacy challenges.
  • To enhance the reliability and confidentiality of user information in smart home environments.
  • To develop a hybrid computing system integrating edge, fog, and cloud architectures for real-time IoT data processing.

Main Methods:

  • Implemented a hybrid cryptosystem combining Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) for data protection.
  • Evaluated four Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, Elliptic Envelope) and five machine learning classification algorithms.
  • Developed an Artificial Neural Network (ANN) dynamic algorithm for hybrid computing system integration and offloading decision-making.

Main Results:

  • TIBDA demonstrated a 10-20% reduction in response time and 5-15% higher AUC values for security compared to other systems.
  • The framework achieved 10-12% greater uptime, indicating superior trustworthiness.
  • Isolation Forest achieved 99.30% accuracy, Random Forest 94.70%, and the ANN model reached 99% validation accuracy with 0.11 loss.

Conclusions:

  • The TIBDA framework significantly outperforms existing systems in response time, security, and trustworthiness for smart home IoT applications.
  • The hybrid cryptosystem and AI-driven approach effectively mitigate data security and privacy concerns.
  • TIBDA offers a robust solution for real-time data processing and enhanced resource utilization in smart living environments.